function policy_effects = policy_effects_counter1(data_counterfactual,data_baseline,fp)

cd(fp.paper)

%Pre-define matrices used to collect results;
data_movers_base = NaN(4,fp.n_rip_sim);
data_movers_counter = NaN(4,fp.n_rip_sim);

for h = 1:1:fp.n_rip_sim %loop over various simulations;

    for age = 9:1:11

        skills_children_age = data_baseline(  data_baseline(:,fp.ind_data.child_age,h)==age , fp.ind_data.child_C , h ) ;

        data_movers_base(age-8,h) =   log(  skills_children_age(fp.id_moved_children(:,h)==1)  )  ;

        skills_children_age = data_counterfactual( data_counterfactual(:,fp.ind_data.child_age,h)==age & data_counterfactual(:,fp.ind_data.moved,h)==1, fp.ind_data.child_C , h ) ;

        data_movers_counter(age-8,h) =   log(  skills_children_age ) ;


        if age==11

            skills_children_age = data_baseline(  data_baseline(:,fp.ind_data.child_age,h)==age , fp.ind_data.child_C_tp1 , h ) ;

            data_movers_base(age-7,h) =   log(  skills_children_age(fp.id_moved_children(:,h)==1)  )  ;

            skills_children_age = data_counterfactual( data_counterfactual(:,fp.ind_data.child_age,h)==age & data_counterfactual(:,fp.ind_data.moved,h)==1, fp.ind_data.child_C_tp1 , h ) ;

            data_movers_counter(age-7,h) =   log(  skills_children_age ) ;

        end

    end

end


pe_skills_t_moved = [nanmean(data_movers_counter,2)'; nanmean(data_movers_base,2)'];


h7=figure;
plot(9:12,pe_skills_t_moved(2,:),'-bs','LineWidth',2, 'MarkerEdgeColor','k', 'MarkerFaceColor','g','MarkerSize',10)
hold on
plot(9:12,pe_skills_t_moved(1,:),'-rs','LineWidth',2, 'MarkerEdgeColor','k', 'MarkerFaceColor','g','MarkerSize',10)
legend('Baseline Dynamics','Counterfactual Dynamics','Location','NorthWest')
set(gca,'XTick',9:1:12)
xlabel('School Grade');
ylabel('Treatment Effect on Skills');
%hgexport(h7, 'TE.png',hgexport('factorystyle'), 'Format', 'png');
%hgexport(h7, 'TE.eps',hgexport('factorystyle'), 'Format', 'eps');
cd(fp.matlab)

end